from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFCvtForImageClassification, TFCvtModel
      from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   self.config_class(**self.inputs_dict )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''embed_dim''' ) )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''num_heads''' ) )






class 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=13			,			lowerCAmelCase=64			,			lowerCAmelCase=3			,			lowerCAmelCase=[16, 48, 96]			,			lowerCAmelCase=[1, 3, 6]			,			lowerCAmelCase=[1, 2, 10]			,			lowerCAmelCase=[7, 3, 3]			,			lowerCAmelCase=[4, 2, 2]			,			lowerCAmelCase=[2, 1, 1]			,			lowerCAmelCase=[2, 2, 2]			,			lowerCAmelCase=[False, False, True]			,			lowerCAmelCase=[0.0, 0.0, 0.0]			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1e-12			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=2			,			)							->       str:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   parent
     SCREAMING_SNAKE_CASE__: int=   batch_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   image_size
     SCREAMING_SNAKE_CASE__: List[Any]=   patch_sizes
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   patch_stride
     SCREAMING_SNAKE_CASE__: List[str]=   patch_padding
     SCREAMING_SNAKE_CASE__: str=   is_training
     SCREAMING_SNAKE_CASE__: int=   use_labels
     SCREAMING_SNAKE_CASE__: Optional[Any]=   num_labels
     SCREAMING_SNAKE_CASE__: int=   num_channels
     SCREAMING_SNAKE_CASE__: str=   embed_dim
     SCREAMING_SNAKE_CASE__: Optional[int]=   num_heads
     SCREAMING_SNAKE_CASE__: int=   stride_kv
     SCREAMING_SNAKE_CASE__: Dict=   depth
     SCREAMING_SNAKE_CASE__: Optional[Any]=   cls_token
     SCREAMING_SNAKE_CASE__: int=   attention_drop_rate
     SCREAMING_SNAKE_CASE__: Any=   initializer_range
     SCREAMING_SNAKE_CASE__: int=   layer_norm_eps
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     SCREAMING_SNAKE_CASE__: List[str]=   None
     if self.use_labels:
      # create a random int32 tensor of given shape
      SCREAMING_SNAKE_CASE__: Dict=   ids_tensor([self.batch_size]			,			self.num_labels )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.get_config()
     return config, pixel_values, labels
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return CvtConfig(
         image_size=self.image_size			,			num_labels=self.num_labels			,			num_channels=self.num_channels			,			embed_dim=self.embed_dim			,			num_heads=self.num_heads			,			patch_sizes=self.patch_sizes			,			patch_padding=self.patch_padding			,			patch_stride=self.patch_stride			,			stride_kv=self.stride_kv			,			depth=self.depth			,			cls_token=self.cls_token			,			attention_drop_rate=self.attention_drop_rate			,			initializer_range=self.initializer_range			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: List[str]=   TFCvtModel(config=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			training=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   (self.image_size, self.image_size)
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[int]=   image_size[0], image_size[1]
     for i in range(len(self.depth ) ):
      SCREAMING_SNAKE_CASE__: List[Any]=   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
     self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.embed_dim[-1], height, width) )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.num_labels
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFCvtForImageClassification(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			labels=lowerCAmelCase			,			training=lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )



    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Tuple=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: int=   config_and_inputs
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_tf
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
    __a						=   (
        {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
        if is_tf_available()
        else {}
    )
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFCvtModelTester(self )
     SCREAMING_SNAKE_CASE__: List[Any]=   TFCvtConfigTester(self			,			config_class=lowerCAmelCase			,			has_text_modality=lowerCAmelCase			,			hidden_size=37 )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     self.config_tester.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
    @unittest.skip(reason='''Cvt does not output attentions''' )
    def     UpperCamelCase_					(	self )							->       str:
     pass
    @unittest.skip(reason='''Cvt does not use inputs_embeds''' )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     pass
    @unittest.skip(reason='''Cvt does not support input and output embeddings''' )
    def     UpperCamelCase_					(	self )							->       int:
     pass
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0			,			reason='''TF does not support backprop for grouped convolutions on CPU.'''			,			)
    def     UpperCamelCase_					(	self )							->       str:
     super().test_dataset_conversion()
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0			,			reason='''TF does not support backprop for grouped convolutions on CPU.'''			,			)
    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     super().test_keras_fit()
    @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__: Optional[int]=   tf.keras.mixed_precision.Policy('''mixed_float16''' )
     tf.keras.mixed_precision.set_global_policy(lowerCAmelCase )
     super().test_keras_fit()
     tf.keras.mixed_precision.set_global_policy('''float32''' )
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: List[str]=   model_class(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Dict=   inspect.signature(model.call )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      SCREAMING_SNAKE_CASE__: Optional[Any]=   [*signature.parameters.keys()]

      SCREAMING_SNAKE_CASE__: List[str]=   ['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     def check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Optional[Any]=   model_class(lowerCAmelCase )

      SCREAMING_SNAKE_CASE__: int=   model(**self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase ) )
      SCREAMING_SNAKE_CASE__: List[Any]=   outputs.hidden_states

      SCREAMING_SNAKE_CASE__: str=   len(self.model_tester.depth )
      self.assertEqual(len(lowerCAmelCase )			,			lowerCAmelCase )

      # verify the first hidden states (first block)
      self.assertListEqual(
          list(hidden_states[0].shape[-3:] )			,			[
              self.model_tester.embed_dim[0],
              self.model_tester.image_size // 4,
              self.model_tester.image_size // 4,
          ]			,			)

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   True
      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      SCREAMING_SNAKE_CASE__: Optional[int]=   True

      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: int=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )



    @slow
    def     UpperCamelCase_					(	self )							->       int:
     for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__: Any=   TFCvtModel.from_pretrained(lowerCAmelCase )
      self.assertIsNotNone(lowerCAmelCase )







def       A__   (						):
 SCREAMING_SNAKE_CASE__: Optional[int]=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_tf
@require_vision
class 		_lowerCamelCase		( unittest.TestCase     ):
    @cached_property
    def     UpperCamelCase_					(	self )							->       List[Any]:
     return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )



    @slow
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

     SCREAMING_SNAKE_CASE__: Tuple=   self.default_image_processor
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   prepare_img()
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_processor(images=lowerCAmelCase			,			return_tensors='''tf''' )

     # forward pass
     SCREAMING_SNAKE_CASE__: List[str]=   model(**lowerCAmelCase )

     # verify the logits
     SCREAMING_SNAKE_CASE__: Optional[Any]=   tf.TensorShape((1, 1000) )
     self.assertEqual(outputs.logits.shape			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   tf.constant([0.9285, 0.9015, -0.3150] )
     self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,			lowerCAmelCase			,			atol=1e-4 ) )






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






